Ph.D, M.E

Dr. P. Krishnakumar currently serves as Assistant Professor (SG) &  Deputy Controller of Examinations in the Department of Mechanical Engineering, and also as Deputy Controller of Examinations for the School of Engineering, Coimbatore Campus. His areas of research include Condition Monitoring, High Speed Machining, Predictive Analytics and Finite Element Modelling of Machining Processes.

Educational Qualification

Name of the Examination University /Board Year of Passing Subjects
Doctoral Degree(Ph, D.) Amrita Vishwa Vidyapeetham 2017 Condition Monitoring
Master’s Degree (ME) Bharathiar University Coimbatore 2000 Computer Integrated Manufacturing
Bachelor’s Degree (BE) Bharathiar University Coimbatore 1998 Mechanical Engineering

Experience Details

Year Affiliation
30-09-2010 - Till Date Assistant Professor (SG ) & Deputy controller of Examinations
Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore
07-07-2007 - 30-09-2010 Assistant Professor (Selection Grade )
Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore
01-07-2004 - 30-06-2007 Senior Lecturer
Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore
02-02-2000 - 31-06-2004 Lecturer
Amrita School of Engineering, Coimbatore

Thrust Area of Research: High Speed Machining, Process Modeling and Predictive Analytics

List of Completed Funded Projects

S. No. Name of the Project Name of the Funding Agency Status Submitted/On-going / Completed Project Grant / Assistance (Rs.) Duration of the Project
1. Process Monitoring and Control of Ultra Precision Machining of Titanium alloys DRDO Completed June 2012 14.00 Lakhs 2 years 3 months
2. Fault diagnosis of dynamic mechanical systems based on signal processing using machine learning techniques DRDO Completed June 2015 28.89 Lakhs 3 years
3 Investigations into the surface integrity of Ti alloys during high speed machining AR&DB Completed Sept. 2016 Rs.9.06 Lakhs 2 Years

Teaching/Research Interests

Major Subjects Taught

  • Mechanics of Solids
  • Finite Element Method
  • Mechanical Vibrations
  • Analysis of Machining Process

Research Interests

  • High Speed Machining
  • Predictive Analytics using Machine Learning Algorithms
  • Process Modeling- Machining


Publication Type: Journal Article

Year of Publication Title


K. Zacharia and Krishna Kumar P., “Chatter Prediction in High Speed Machining of Titanium Alloy (Ti-6Al-4V) using Machine Learning Techniques”, Materials Today: Proceedings, vol. 24, pp. 350-358, 2020.[Abstract]

Titanium alloys have been extensively utilized in the aerospace and biomedical industries because of higher corrosion resistance and their good strength to weight ratio. In spite of several advantages, titanium alloys are difficult to machine because of their poor thermal conductivity and high chemical reactivity. Identification of suitable machining conditions is the key to get the good surface finish. Chatter during machining brings adverse effects in surface quality, dimensional accuracy and in tool life. The objective of this work is to identify chatter free machining conditions for machining titanium alloys and to predict the chatter occurrence with the help of machine leaning algorithms. During machining of titanium alloy, the vibration signals are captured for various machining conditions using accelerometer. From the raw signal statistical features are extracted and decision tree algorithm is used in selecting the dominant features. By monitoring the dominant features, chatter occurrences are predicted using Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machines (SVM).

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S. P. Krishnan, K. Ramesh Kumar, and Krishna Kumar P., “Hidden Markov Modelling of High-Speed Milling (HSM) Process Using Acoustic Emission (AE) Signature for Predicting Tool Conditions”, Advances in Materials and Manufacturing Engineering, 2020.[Abstract]

Tool condition monitoring is an important activity to monitor and maintain the quality of products manufactured in any machining process without any manual intervention. Hidden Markov models (HMM) are developed in this study for predicting tool conditions in a High-Speed Milling of titanium alloy using a carbide tool. Tool conditions are predicted using AE signatures captured during the metal cutting operation. A correlation between AE features and tool conditions were established using Baum-Welch and Viterbi algorithms. HMM models proposed in this study are integrated with the K-means clustering algorithm. The clustered data has been represented as an integer sequence and is divided into 3 tool states such as `sharp', `intermediate' and `worn-out'. Three HMM models are created for each state of the tool. Two AE features namely `Root Mean Square (RMS)' and `Rise' were used for developing HMMs. The performance of the HMMs is evaluated using log-likelihood measure.

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T. Praveenkumar, Dr. Saimurugan M., Krishna Kumar P., and I, R. K., “Fault Diagnosis of Gearbox Using Machine Learning and Deep Learning Techniques”, International Journal of Engineering and Advanced Technology, vol. 9, pp. 3940-3943, 2019.


Krishna Kumar P., ,, and Ramachandran, K. I., “Feature level vibration and acoustic emission sensor signal fusion to improve classification efficiency in tool condition monitoring using machine learning classifiers”, International Journal of Prognostics and Health Management, vol. 9, no. 1, 2018.


Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Acoustic emission based tool condition classification in a precision high speed machining of titanium alloy (Ti-6Al-4V): A machine learning approach”, International Journal of Computational Intelligence and Applications, vol. 17, 2018.[Abstract]

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains. © 2018 World Scientific Publishing Europe Ltd.

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Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers”, International Journal of Prognostics and Health Management, vol. 9, no. 8, pp. 2153-2648, 2018.[Abstract]

To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature. © 2018, Prognostics and Health Management Society. All rights reserved.

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K. Ramesh Kumar, Krishna Kumar P., and Ramachandran, K. I., “Machine Learning Based Tool Condition classification using AE and Vibration data in a High Speed Milling Process Using Wavelet Features”, Intelligent Decision Technologies: An International Journal, 2018.


Krishna Kumar P., Sripathi, J., Vijay, P., and Dr. K. I. Ramachandran, “Finite Element Modelling and Residual Stress Prediction in End Milling of Ti6Al4Valloy”, IOP Conference Series: Materials Science and Engineering, vol. 149, p. 012154, 2016.[Abstract]

Titanium and its alloys are materials that exhibit unique combination of mechanical and physical properties that enable their usage in various fields. In spite of having a lot of advantages, their usage is limited because they are difficult to machine due to their inherent properties of high specific heat capacity, reactivity with tool and low thermal conductivity thereby causing excessive tool wear. To facilitate the process of machining, it becomes necessary to find out and relieve the residual stress caused during machining. Since experiments cannot be performed for each instance, creation of an FE model is desirable. In this paper a finite element analysis (FEA) of the machining of Ti6Al4V for different cutting speeds is presented. A 3D finite element model is developed with the Titanium alloy (Ti6Al4V) as the workpiece and a four flute carbide tip end mill cutter as the tool to predict the residual stress developed within the titanium alloy after machining. The finite element model utilises the Johnson-Cook model to depict the plasticity and the damage criteria and implements the Arbitrary Lagrangian Eulerian (ALE) formulation to increase the accuracy of the model. The FE model has been developed and the findings are presented. The results indicate that residual stresses are maximum at the surface and decrease linearly along the depth and increase as the cutting speed and depth of cut are increased.

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M. Saimurugan, Praveenkumar, T., and Krishna Kumar P., “A Study on Classification Ability of Decision Tress and Support Vector Machine in Gear Box Fault Detection”, Applied Mechanics and Materials, pp. 813-814, 2015.


Krishna Kumar P., Rameshkumar, K., and Dr. K. I. Ramachandran, “Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy”, Procedia Computer Science, vol. 50, pp. 270 - 275, 2015.[Abstract]

Ti-6Al-4V is extensively used in aerospace and bio-medical applications. In an automated machining environment monitoring of tool conditions is imperative. In this study, Experiments were conducted to classify the tool conditions during High Speed Machining of Titanium alloy. During the machining process, vibration signals were monitored continuously using accelerometer. The features from the signal are extracted and a set of prominent features are selected using Dimensionality Reduction Technique. The selected features are given as an input to the classification algorithm to decide about the condition of the tool. Feature selection has been carried out using J48 Decision Tree Algorithm. Classifications of tool conditions were carried out using Machine Learning Algorithms namely J48 Decision Tree algorithm and Artificial Neural Network (ANN). From the analysis, it is found that ANN is producing comparatively better results. The methodology adopted in this study will be useful for online tool condition monitoring.

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Dr. Saimurugan M., T. Praveenkumar, Krishna Kumar P., and Ramachandran, K. I., “A Study on the Classification Ability of Decision Tree and Support Vector Machine in Gearbox Fault Detection”, Applied Mechanics and Materials, vol. 813-814, pp. 1058-1062, 2015.


T. Praveenkumar, Dr. Saimurugan M., Krishna Kumar P., and Dr. K. I. Ramachandran, “Fault Diagnosis of Automobile Gearbox Based on Machine Learning Techniques”, Procedia Engineering, vol. 97, pp. 2092–2098, 2014.[Abstract]

Gearbox is an essential device employed in industries to vary speed and load conditions according to the requirements. More advancement in its design and operation leads to increase in industrial applications. The failure in any of the components of gearbox can lead to production loss and increase maintenance cost. The component failure has to be detected earlier to avoid unexpected breakdown. Vibration measurements are used to monitor the condition of the machine for predictive maintenance and to predict the gearbox faults successfully. This paper addresses the use of vibration signal for automated fault diagnosis of gearbox. In the experimental studies, good gears and face wear gears are used to collect vibration signals for good and faulty conditions of the gearbox. Each gear is tested with two different speeds and loading conditions. The statistical features are extracted from the acquired vibration signals. The extracted features are given as an input to the support vector machine (SVM) for fault identification. The Performance of the fault identification system using vibration signals are discussed and compared.

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P. K Marimuthu, Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Finite element simulation of effect of residual stresses during orthogonal machining using ALE approach”, International Journal of Machining and Machinability of Materials, vol. 14, pp. 213–229, 2013.[Abstract]

This paper presents a finite element model that has been developed to predict the effect of residual stress induced in the work material during multiple pass turning of AISI 4340 steel. Chip morphology and force variation during machining are also quantified using the FE model. Finite element model was developed using arbitrary Lagrangian-Eulerian formulation along with Johnson-Cook material model and Johnson-Cook damage model. The finite element model developed in this study was validated experimentally by studying the chip morphologogy and cutting force variation during the machining. Results indicate that there is good correlation existing between numerical results and experimental results.

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K. Rameshkumar, Sumesh, A., Krishna Kumar P., and T, C. Austin V., “Productivity Improvement of a Manufacturing Enterprise using Lean Tools: A Case Study in Discrete Manufacturing Sector”, Indore Management Journal, vol. 3, no. 2, pp. 34-4, 2012.

Publication Type: Conference Proceedings

Year of Publication Title


Krishna Kumar P., Vishnu, J., Ramachandran, K. I. ., and Rameshkumar, K., “Finite Element Modelling of Residual Stress in High Speed Machining of Titanium Alloy”, CAE international conference, IIT, Chennai. 2013.


Krishna Kumar P., K., R., and Ramachandran, K. I., “Vibration based Tool Condition Monitoring (TCM) in machining of Titanium alloy (Ti-6Al-4V) using machine Learning Algorithms”, International Conference on Optimization, Computing & Business Analytics (ICOCBA 2012).- ORSI International Conference. 2012.


K. Ramesh Kumar, Krishna Kumar P., and A. Sumesh, “Productivity Improvement of a Manufacturing Industry Using Value Stream Mapping (VSM) Approach: A case Study in a Discrete Ma1nufacturing Sector”, International Conference on operational research for urban and rural development (ORURD). 2010.


Krishna Kumar P. and K, R., “Simulation optimization in a Kanban Controlled Flowshop”, ORSI International Conference. 2009.


Krishna Kumar P. and K, R., “Finite Difference Modelling of Laser Drilling for Machining Silicon Carbide”. National Engineering College, Kovilpatti., 2009.


Krishna Kumar P. and K, R., “End milling process parameter optimization using Particle Swarm Optimization Algorithms”, International conference on Emerging Research and Advances in Mechanical Engineering. Velammal Engineering College, Chennai., 2009.

Publication Type: Conference Paper

Year of Publication Title


A. Sumesh, K. Ramesh Kumar, and Krishna Kumar P., “Simulation Optimization in a Kanban Controlled Flow Shop”, in ORSI – 2008/TIRUPATHI, 2008.

Research Projects (Co-PI)

  • Process Monitoring and Control of Ultra Precision Machining of Titanium alloys, sponsored by DRDO, Duration 2010- 2012.
  • Fault diagnosis of dynamic mechanical systems based on signal processing using machine learning techniques, sponsored by DRDO, Duration 2012- 2015.
  • Investigations into the surface integrity of Ti alloys during high speed machining, , sponsored by AR&DB, Duration 2014- 2016.

List of Ph.D. Students

Current: 1: Co Guide: Mr. Jithin Jose – Working on remaining useful life prediction using Deep Learning Algorithms

Industrial Collaboration/Consultancy

  • DRDO- Machinability Study- High Speed Machining of Titanium Alloy Ti 6Al 4V & Fault Diagnosis of Mechanical Systems using Machine Learning Algorithms
  • ARDB –Surface Integrity Study in Machining of Titanium alloys and Residual Stress Measurement.

Conferences/Workshops/Short-term courses (Organized)

  • Organized national level workshops on Recent Trends in Manufacturing sponsored by DRDO and ISRO during the year 2011 and 2014.
  • Organized national level workshops Recent trends in signature analysis of arc welding March 6, 2017

Key Responsibilities at Amrita Vishwa Vidyapeetham

  • M Tech Manufacturing Engineering Program Coordinator
  • Lab In charge- Special Machines
  • Member- Department Purchase Committee
  • Deputy Controller of Examinations, Amrita School of Engineering, Coimbatore Campus

Membership in Professional Bodies

  • Life member –ISTE

Other Achievements / Activities

  • BoS- Member – M Tech Manufacturing Engineering
Faculty Research Interest: